Dataset name,Metric name,AutoGluon,FEDOT,H2O,LAMA
APSFailure,auc,0.99,0.991,0.992,0.992
Amazon_employee_access,auc,0.857,0.865,0.873,0.879
Australian,auc,0.94,0.939,0.939,0.945
Covertype,neg_logloss,-0.071,-0.117,-0.265,
Fashion-MNIST,neg_logloss,-0.329,-0.373,-0.38,-0.248
Jannis,neg_logloss,-0.728,-0.737,-0.691,-0.664
KDDCup09_appetency,auc,0.804,0.822,0.829,0.85
MiniBooNE,auc,0.982,0.981,,0.988
Shuttle,neg_logloss,-0.001,-0.001,-0.0,-0.001
Volkert,neg_logloss,-0.917,-1.097,-0.976,-0.806
adult,auc,0.91,0.925,0.931,0.932
bank-marketing,auc,0.931,0.935,0.939,0.94
blood-transfusion,auc,0.69,0.759,0.765,0.75
car,neg_logloss,-0.117,-0.011,-0.004,-0.002
christine,auc,0.804,0.812,0.823,0.83
cnae-9,neg_logloss,-0.332,-0.211,-0.175,-0.156
connect-4,neg_logloss,-0.502,-0.456,-0.338,-0.337
credit-g,auc,0.795,0.778,0.789,0.796
dilbert,neg_logloss,-0.148,-0.159,-0.05,-0.033
fabert,neg_logloss,-0.788,-0.895,-0.752,-0.766
guillermo,auc,0.9,0.891,,0.926
jasmine,auc,0.883,0.888,0.887,0.88
jungle chess,neg_logloss,-0.431,-0.193,-0.24,-0.149
kc1,auc,0.822,0.843,,0.831
kr-vs-kp,auc,0.999,1.0,,1.0
mfeat-factors,neg_logloss,-0.161,-0.094,,-0.082
nomao,auc,0.995,0.994,0.996,0.997
numerai28_6,auc,0.517,0.529,0.531,0.531
phoneme,auc,0.965,0.965,,0.965
segment,neg_logloss,-0.094,-0.062,,-0.061
sylvine,auc,0.985,0.988,,0.988
vehicle,neg_logloss,-0.515,-0.354,,-0.404
